Integrated network pharmacology and molecular modeling approaches to reveal the anti-diabetic potential of phytochemicals from Zanthoxylum simulans

Background Zanthoxylum simulans , traditionally used for diabetes, lacks molecular mechanistic insights. This study integrates network pharmacology, molecular docking, ADMET profiling, 200-ns molecular dynamics, PCA, and FEL analyses to identify anti-diabetic phytochemicals from its fruit and bark. Methods From 120 literature-identified phytochemicals, five leads were screened (MolSoft DL ≥ 0.18): magnocurarine (1.45), N-methyltetrahydrocolumbamine (1.30), reticuline (1.13), β-sitostenone (0.91), menisperine (0.87). SwissTargetPrediction yielded 500 targets overlapping 27 diabetes genes (DisGeNET), identifying hub proteins PPARG, IL6, TNF, STAT3 via Cytoscape/STRING. Docking used AutoDock Vina; ADMET via SwissADME/pkCSM; Molecular Dynamics (200 ns GROMACS) with RMSD/RMSF/Rg/SASA/H-bond analysis; PCA/FEL via Bio3D/OriginPro. Results β-Sitostenone showed the highest docking affinities (-8.5 kcal/mol STAT3, -8.2 kcal/mol PPARG); menisperine -8.1 kcal/mol STAT3. All leads had optimal ADMET (no hepatotoxicity/hERG inhibition; LD50 2.356–2.531 mol/kg). Magnocurarine-PPARG exhibited superior stability vs pioglitazone: stable RMSD (< 0.30 nm), low RMSF (0.05–0.15 nm), compact Rg (1.92–1.98 nm), reduced SASA (142–150 nm 2 ), consistent H-bonds (2–4), and broader FEL basic indicating entropic flexibility. Conclusion Magnocurarine emerges as a superior anti-diabetic lead from Zanthoxylum simulans , demonstrating exceptional PPARG binding, pharmacokinetic safety, and dynamic stability over 200 ns validating its therapeutic potential.